blackThe encoder uses VGG16 [1]} without pooling layers as the backbone, followed by an ASPP module to capture multi-scale visual information, and then the decoder restores the original image resolution by stacking convolution and up-sampling layers. Since how to achieve personalization in PSP is not the focus of this study, for simplicity, we use a plain but effective method to achieve personalization, which is to train user saliency models separately on different user datasets to obtain personalized behaviors. Please note that similar to the TD module, the PSP module is also a replaceable plugin that can be replaced with other pretrained personalized saliency models, depending on the researcher's preference. For instance, treat each user's prediction task separately and use a multi-task model to train a personalized saliency model for each user [2]}, or train a meta-learning model that can quickly adapt to new personalized tasks [3]}.
